S&P 500

Clase series de tiempo, primavera 2024

Datos

library(tidyquant)

Loading required package: lubridate

Attaching package: ‘lubridate’

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Loading required package: PerformanceAnalytics
Loading required package: xts
Loading required package: zoo

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Attaching package: ‘PerformanceAnalytics’

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Loading required package: quantmod
Loading required package: TTR
Registered S3 method overwritten by 'quantmod':
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  as.zoo.data.frame zoo 

library(tidyverse)

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library(tsibble)

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library(fable)

Loading required package: fabletools

Attaching package: ‘fable’

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library(feasts)

index = tq_index("SP500") %>% select(symbol, weight)

Getting holdings for SP500

index = index %>% filter(symbol != "-")
index

sum(index$weight)

[1] 0.9961429

data = tq_get(index$symbol, get = "stock.prices", from = "2022-01-01", to = "2024-03-29") %>% select(symbol, date, adjusted)

Warning: There were 2 warnings in `dplyr::mutate()`.
The first warning was:
ℹ In argument: `data.. = purrr::map(...)`.
Caused by warning:
! x = 'GEV', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "GEV", env = <environment>, verbose = FALSE, : Unable to import “GEV”.
HTTP error 400.
 Removing GEV.
ℹ Run ]8;;ide:run:dplyr::last_dplyr_warnings()dplyr::last_dplyr_warnings()]8;; to see the 1 remaining
  warning.

symbols_from_data = unique(data$symbol)
data = pivot_wider(data, id_cols = date, names_from = symbol, values_from = adjusted)
data = mutate(data, t = row_number())
data = pivot_longer(data, all_of(symbols_from_data), names_to = "symbol", values_to = "adjusted")
data = as_tsibble(data, index = t, key = symbol)

Split de datos

train = data %>% filter_index(. ~ (562-30))
test = data %>% filter_index((562-30+1) ~ .)

Modelos

ETS y ARIMA

sp500_arima_ets = train %>% model(arima = ARIMA(adjusted),
                                  ets = ETS(adjusted))

Warning: NaNs producedWarning: NaNs producedWarning: NaNs producedWarning: NaNs producedWarning: NaNs producedWarning: NaNs producedWarning: NaNs producedWarning: NaNs producedWarning: NaNs producedWarning: NaNs producedWarning: NaNs produced

sp500_arima_ets

ticker = "ABNB"
sp500_arima_ets %>% filter(symbol == ticker) %>% augment() |>
  ggplot(aes(x = t)) +
  geom_line(aes(y = adjusted, colour = "reales")) +
  geom_line(aes(y = .fitted, colour = "ajustados")) +
  labs(y = NULL,
    title = ticker
  ) +
  guides(colour = guide_legend(title = NULL))

fit_accuracy <- sp500_arima_ets %>% forecast(h = 30) %>% accuracy(test, measures = lst(MAPE)) %>% 
  pivot_wider(names_from = .model, values_from = MAPE) %>% 
  select(-.type)
fit_accuracy

best_fit <- sp500_arima_ets %>% 
  transmute(
    symbol, # Need to keep key variables for a valid mable
    best_fit = if_else(fit_accuracy$ets < fit_accuracy$arima, ets, arima)
  )
best_fit